knowengdev/autobuild_geneset

•Updated about 10 years ago

Test build for geneset_characterization_pipeline

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986

knowengdev/autobuild_geneset repository overview

⁠Gene Set Characterization Pipeline

This is the Knowledge Engine for Genomics (KnowEnG), an NIH BD2K Center of Excellence, Gene Set Characterization Pipeline.
This pipeline selects one of three methods to rank a user supplied gene set against a KnowEnG gene sets collection.

One can select one of three gene set characterization options:

Options	Method	Parameters
Fisher exact test	Fisher	fisher
Discriminative Random Walks with Restart	DRaWR	DRaWR
Net One	Net One	net_one

⁠How to run this pipeline with provided data

###1. Get Access to KnowEnG-Research Repo: Email [email protected]⁠ infrastructure team (IST) lead to:

Access KnowEnG-Research github repo

###2. Clone the Samples_Clustering_Pipeline Repo

 git clone https://github.com/KnowEnG-Research/GeneSet_Characterization_Pipeline.git

###3. Install the following (Mac OS or Linux)

 apt-get install -y python3-pip
 apt-get install -y libblas-dev liblapack-dev libatlas-base-dev gfortran
 pip3 install numpy==1.11.1
 pip3 install pandas==0.18.1 
 pip3 install scipy==0.18.0
 pip3 install scikit-learn==0.17.1
 apt-get install -y libfreetype6-dev libxft-dev 
 pip3 install matplotlib==1.4.2
 pip3 install pyyaml
 pip3 install knpackage

###4. Change directory to the GeneSet_Characterization_Pipeline

 cd GeneSet_Characterization_Pipeline

###5. Use the following "make" command to create a local directory "run_dir" and place all the parameters files in it

Prepare input data and running directories.

 make preparation

Run the pipeline you desire

 make -f Makefile.docker docker_run_fisher
 make -f Makefile.docker docker_run_drawer

Clean the running environment and revert the local repository to original state after you finish your tests and analysis

 make final_clean

⁠How to run it with your data

###6. Setup your run environment

Create a run directory

mkdir run_directory_name

Create results directory to save output files under run directory

cd run_directory_name
mkdir results_directory_name

Create run_paramerters file with example below (yml format)

Key	Value	Comments
method	DRaWR or fisher	Choose DRaWR or fisher as the gene set characterization method
pg_network_name_full_path	./a/pg_network_name	Path and file name of the 4 col property file
gg_network_name_full_path	./a/gg_network_name	Path and file name of the 4 col network file(only needed in DRaWR)
spreadsheet_name_full_path	./a/spreadsheet_name	Path and file name of user supplied gene sets
results_directory	./run_dir/results	Directory to save the output files
rwr_max_iterations	500	Maximum number of iterations without convergence in random walk with restart(only needed in DRaWR)
rwr_convergence_tolerence	0.0001	Frobenius norm tolerence of spreadsheet vector in random walk(only needed in DRaWR)
rwr_restart_probability	0.5	alpha in `Vn+1 = alpha * N * Vn + (1-alpha) * Vo`(only needed in DRaWR)
a = input_date
pg_network_name = kegg_pathway_property_gene
gg_network_name = STRING_experimental_gene_gene
spreadsheet_name = ProGENI_rwr20_STExp_GDSC_500.rname.gxc

Make sure the directories of the input data in fisher_run_file.yml and DRaWR_run_file.yml are correct
Run GeneSet_Characterization_Pipeline

export PYTHONPATH='../GeneSet_Characterization_Pipeline/src':$PYTHONPATH    
python3 ../GeneSet_Characterization_Pipeline/src/geneset_characterization.py -run_directory ./ -run_file file_name.yml

Output files are saved in results directory
1.DRaWR_result output file saves sorted properties based on the difference between updated user gene vector and baseline.
2.fisher_result output file has seven columns and it is sorted in ascending order based on pval.

user gene	property	count	user count	gene count	overlap	pval
string	string	int	int	int	int	float

⁠Net_One

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about 10 years ago

docker pull knowengdev/autobuild_geneset